Car License Plates Extraction and Recognition Based on Connected Components Analysis and HMM Decoding

2005 ◽  
pp. 571-578 ◽  
Author(s):  
David Llorens ◽  
Andrés Marzal ◽  
Vicente Palazón ◽  
Juan M. Vilar
Keyword(s):  
Connected Components ◽  
Components Analysis ◽  
Connected Components Analysis

An Integrated Clustering Algorithm and its Application in Surgical Navigation

2013 ◽  
Vol 790 ◽  
pp. 623-626
Author(s):  
Shi Ju Yan
Keyword(s):  
Camera Calibration ◽  
Clustering Algorithm ◽  
Surgical Navigation ◽  
Geometric Parameters ◽  
Connected Components ◽  
Analysis Method ◽  
Fluoroscopic Image ◽  
Parameters Extraction ◽  
Components Analysis ◽  
Connected Components Analysis

Extracting landmark geometric parameters in fluoroscopic image is a key technique in the camera calibration process of C-arm-based surgical navigation. This paper proposes an integrated clustering algorithm for landmark geometric parameters extraction. The proposed algorithm integrates an adaptive thresholding method and a connected components analysis method, it needs only one pass to process grayscale images and outcomes landmarks parameters directly. By experiments, the proposed algorithm demonstrates its high robustness, reliability and running efficiency in landmark geometric parameters extraction.

Structure and Visualization of High-Dimensional Conductance Spaces

2006 ◽  
Vol 96 (2) ◽  
pp. 891-905 ◽  
Author(s):  
Adam L. Taylor ◽  
Timothy J. Hickey ◽  
Astrid A. Prinz ◽  
Eve Marder
Keyword(s):  
Connected Components ◽  
High Dimensional ◽  
Intrinsic Properties ◽  
Calcium Dependent ◽  
Linear Classifier ◽  
Burst Period ◽  
Separating Hyperplane ◽  
Different Types ◽  
Components Analysis ◽  
Connected Components Analysis

Neurons, and realistic models of neurons, typically express several different types of voltage-gated conductances. These conductances are subject to continual regulation. Therefore it is essential to understand how changes in the conductances of a neuron affect its intrinsic properties, such as burst period or delay to firing after inhibition of a particular duration and magnitude. Even in model neurons, it can be difficult to visualize how the intrinsic properties vary as a function of their underlying maximal conductances. We used a technique, called clutter-based dimension reordering (CBDR), which enabled us to visualize intrinsic properties in high-dimensional conductance spaces. We applied CBDR to a family of models with eight different types of voltage- and calcium-dependent channels. CBDR yields images that reveal structure in the underlying conductance space. CBDR can also be used to visualize the results of other types of analysis. As examples, we use CBDR to visualize the results of a connected-components analysis, and to visually evaluate the results of a separating-hyperplane (i.e., linear classifier) analysis. We believe that CBDR will be a useful tool for visualizing the conductance spaces of neuronal models in many systems.

POSE-EXPRESSION NORMALIZATION FOR FACE RECOGNITION USING CONNECTED COMPONENTS ANALYSIS

2006 ◽  
Vol 20 (06) ◽  
pp. 869-881
Author(s):  
JAE-YOUNG CHOI ◽  
TAEG-KEUN WHANGBO ◽  
YOUNG-GYU YANG ◽  
MURLIKRISHNA VISWANATHAN ◽  
NAK-BIN KIM
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Connected Components ◽  
Recognition Method ◽  
Facial Region ◽  
Detection Model ◽  
Recognition Systems ◽  
Components Analysis ◽  
Empirical Tests ◽  
Connected Components Analysis

Accurate measurement of poses and expressions can increase the efficiency of recognition systems by avoiding the recognition of spurious faces. This paper presents a novel and robust pose-expression invariant face recognition method in order to improve the existing face recognition techniques. First, we apply the TSL color model for detecting facial region and estimate the vector X-Y-Z of face using connected components analysis. Second, the input face is mapped by a deformable 3D facial model. Third, the mapped face is transformed to the frontal face which appropriates for face recognition by the estimated pose vector and action unit of expression. Finally, the damaged regions which occur during the process of normalization are reconstructed using PCA. Several empirical tests are used to validate the application of face detection model and the method for estimating facial poses and expression. In addition, the tests suggest that recognition rate is greatly boosted through the normalization of the poses and expression.

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